summary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D Materials Database Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5255 materials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mcosta/Desktop/jupyterlab/venv/lib/python3.6/site-packages/pandas/core/indexes/api.py:107: RuntimeWarning: '<' not supported between instances of 'str' and 'int', sort order is undefined for incomparable objects\n",
      "  result = result.union(other)\n",
      "/home/mcosta/Desktop/jupyterlab/venv/lib/python3.6/site-packages/pandas/core/indexes/api.py:69: RuntimeWarning: '<' not supported between instances of 'int' and 'str', sort order is undefined for incomparable objects\n",
      "  index = _union_indexes(indexes, sort=sort)\n",
      "/home/mcosta/Desktop/jupyterlab/venv/lib/python3.6/site-packages/pandas/core/indexes/api.py:107: RuntimeWarning: '<' not supported between instances of 'int' and 'str', sort order is undefined for incomparable objects\n",
      "  result = result.union(other)\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "materials=[]\n",
    "with open('db.json', 'r') as file:\n",
    "    for line in file:\n",
    "        materials.append(json.loads(line))\n",
    "\n",
    "print(\"Found %s materials\" % len(materials))\n",
    "\n",
    "import pandas as pd\n",
    "    \n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "materials_df = pd.DataFrame.from_dict(materials)\n",
    "materials_df.set_index('material_id', inplace=True)\n",
    "\n",
    "# drop unnecessary columns\n",
    "for col in ['_id','_tasksbuilder','created_at']:\n",
    "    if col in materials_df.columns:\n",
    "        materials_df.drop(col, axis=1, inplace=True)\n",
    "\n",
    "# expand dictionaries in cells\n",
    "for dict_column in ['bandstructure', 'calc_settings', 'spacegroup', 'thermo']:\n",
    "    if dict_column in materials_df.columns:\n",
    "        materials_df=materials_df.join(materials_df[dict_column].apply(pd.Series),lsuffix='_orig')\n",
    "        materials_df.drop([dict_column], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram of bandgaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'bandgap (eV)')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=materials_df['bandgap'].hist()\n",
    "ax.set_xlabel('bandgap (eV)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram of number of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'number of atomic species')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=materials_df['nelements'].hist()\n",
    "ax.set_xlabel('number of atomic species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most common anonynous formula of binary compounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=materials_df['formula_anonymous'].where(materials_df['nelements'] == 2).value_counts()[:10].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most common crystal systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=materials_df['crystal_system'].value_counts()[:10].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most common spacegroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=materials_df['sg_symbol'].value_counts()[:10].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ..."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}